Approximate message passing for amplitude based optimization

Junjie Ma, Ji Xu, Arian Maleki
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3365-3374, 2018.

Abstract

We consider an ℓ2-regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message passing algorithm that aims to solve this optimization problem. We consider the asymptotic setting m,n→∞, m/n→δ and obtain sharp performance bounds, where m is the number of measurements and n is the signal dimension. We show that for complex signals the algorithm can perform accurate recovery with only m=(64π2−4)n≈2.5n measurements. Also, we provide sharp analysis on the sensitivity of the algorithm to noise. We highlight the following facts about our message passing algorithm: (i) Adding ℓ2 regularization to the non-convex loss function can be beneficial even in the noiseless setting; (ii) spectral initialization has marginal impact on the performance of the algorithm.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-ma18e, title = {Approximate message passing for amplitude based optimization}, author = {Ma, Junjie and Xu, Ji and Maleki, Arian}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {3365--3374}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/ma18e/ma18e.pdf}, url = {https://proceedings.mlr.press/v80/ma18e.html}, abstract = {We consider an $\ell_2$-regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message passing algorithm that aims to solve this optimization problem. We consider the asymptotic setting $m,n \rightarrow \infty$, $m/n \rightarrow \delta$ and obtain sharp performance bounds, where $m$ is the number of measurements and $n$ is the signal dimension. We show that for complex signals the algorithm can perform accurate recovery with only $m=\left ( \frac{64}{\pi^2}-4\right)n\approx 2.5n$ measurements. Also, we provide sharp analysis on the sensitivity of the algorithm to noise. We highlight the following facts about our message passing algorithm: (i) Adding $\ell_2$ regularization to the non-convex loss function can be beneficial even in the noiseless setting; (ii) spectral initialization has marginal impact on the performance of the algorithm.} }
Endnote
%0 Conference Paper %T Approximate message passing for amplitude based optimization %A Junjie Ma %A Ji Xu %A Arian Maleki %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-ma18e %I PMLR %P 3365--3374 %U https://proceedings.mlr.press/v80/ma18e.html %V 80 %X We consider an $\ell_2$-regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message passing algorithm that aims to solve this optimization problem. We consider the asymptotic setting $m,n \rightarrow \infty$, $m/n \rightarrow \delta$ and obtain sharp performance bounds, where $m$ is the number of measurements and $n$ is the signal dimension. We show that for complex signals the algorithm can perform accurate recovery with only $m=\left ( \frac{64}{\pi^2}-4\right)n\approx 2.5n$ measurements. Also, we provide sharp analysis on the sensitivity of the algorithm to noise. We highlight the following facts about our message passing algorithm: (i) Adding $\ell_2$ regularization to the non-convex loss function can be beneficial even in the noiseless setting; (ii) spectral initialization has marginal impact on the performance of the algorithm.
APA
Ma, J., Xu, J. & Maleki, A.. (2018). Approximate message passing for amplitude based optimization. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:3365-3374 Available from https://proceedings.mlr.press/v80/ma18e.html.

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